The Comparative Study of Mining Data Classification Algorithm for Cost Group Determination of Student’s Single Tuition Fee (STF)

The Comparative Study of Mining Data Classification Algorithm for Cost Group Determination of Student’s Single Tuition Fee (STF)

Abstract—Group determination of student’s Single Tuition Fee (STF) by universities is a task analysis of students’ financial ability to determine the group of tuition fees amount by students; thereby it must be conducted carefully. The importance of this determination task of student’s STF amount makes the process requires a lot of time, effort and cost if conducted manually, especially if student’s data analyzed reach thousands of data. To overcome this problem, the use of mining data classification algorithm in this research is explored to find the best algorithm for the case of STF group classification. Some criteria used as a feature to classify group of STF in this research such as the parents’ income, the number of dependents, the regional origin and selected study program cluster. Utilizing machine learning techniques, the results obtained showed that Decision Tree and SVM as algorithms with the highest accuracy of 80%. The determination of the best algorithm between both of them then conducted by applying the rules of fault tolerance. The best-obtained algorithm was finally used to predict the STF group class of each student that amount to 3528 data.

Keywords—classification algorithm; machine learning; mining data; STF

I. INTRODUCTION

The government of Indonesia states that the economic conditions diversity of the communities varies widely, thereby the policy-making in relation to the expenditure of the community for a service should pay attention to the justice aspect by classifying the financial capability of each citizen to adjust the cost amount. This matter underlies the birth of Cross Subsidy policy, not least in the world of education with the application of Single Tuition Fee (STF) which has been regulated in Permenristekdikti Number 22 Year 2015 [1].

In the case of the STF amount group determination, each university should analyze new student data to classify its financial capabilities. The main criteria used, As the part that has been set on [1], it is the parent income criteria. However, the financial ability of a student cannot be seen only from the income amount of his parents alone. Other influential criteria such as the number of siblings, regional origin, and selected study cluster also have an impact on a student’s financial ability.

The number of criteria and the amount of student data that reaches thousands to be analyzed certainly takes a lot of time, effort and cost [2]. It is required a system that can assist in decision making about the STF amount determination of each student with analysis of his financial ability.

II. LITERATURE REVIEW

Single Tuition Fee or commonly referred to as STF is a form of Indonesian government policy to guarantee the right of every citizen to receive a proper education. STF is designed as an educational financing solution at the university level by applying the concept of cross-subsidy. For fees in STF as referred to Article 2 paragraph (2) Permenristekdikti Number 22 Year 2015 [1] consists of several groups determined based on the economic ability of students, students’ parents, or other parties who finance it.

The nature of STF determination works by analyzing a student’s financial ability based on characters that can affect his financial condition. To overcome this problem, mining data techniques are commonly technique used [3]. Mining data is a series of processes for extracting previously unknown new information from a data set [4]. Mining data is mostly conducted to the purposes of classification, prediction, and clustering. For the case of STF determination itself fell on the classification analysis.

Several researches have shown that financial ability analysis could be conducted with mining data for various financial purposes [4] such as Student Determination with Difficult Economic Conditions [3], Prediction of Scholarship Provision Determination [5], up to Classification for Financial Fraud Detection [6].